Advisor

Thompson, David S.

Committee Member

Soni, Bharat K.

Committee Member

Reese, Donna

Committee Member

Fowler, James E.

Date of Degree

1-1-2002

Document Type

Graduate Thesis - Open Access

Major

Computational Engineering (program)

Degree Name

Master of Science

College

College of Engineering

Abstract

Scientific visualization is an essential and indispensable tool for the systematic study of computational (CFD) datasets. There are numerous methods currently used for the unwieldy task of processing and visualizing the characteristically large datasets. Feature extraction is one such technique and has become a significant means for enabling effective visualization. This thesis proposes different modules to refine the maps which are generated from a feature detection on a dataset. The specific example considered in this work is the vortical flow in a two-dimensional oceanographic dataset. This thesis focuses on performing feature extraction by detecting the features and processing the feature maps in three different modules, namely, denoising, segmenting and ranking. The denoising module exploits a wavelet-based multiresolution analysis (MRA). Although developed for two-dimensional datasets, these techniques are directly extendable to three-dimensional cases. A comparative study of the performance of Optimal Feature-Preserving (OFP) filters and non-OFP filters for denoising is presented. A computationally economical implementation for segmenting the feature maps as well as different algorithms for ranking the regions of interest (ROI's) are also discussed in this work.

URI

https://hdl.handle.net/11668/20074

Comments

CFD||ranking||segmenting||denoising||feature preserving||multiresolution analysis||wavelet||feature extraction

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